Correction: Parental supervision positively impacts children's economic prospects two decades later: A prospective longitudinal study.

[This corrects the article DOI: 10.1371/journal.pone.0286218.].

The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health.

Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.

Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning.

Childhood mental health disorders such as anxiety, depression, and ADHD are commonly-occurring and often go undetected into adolescence or adulthood. This can lead to detrimental impacts on long-term wellbeing and quality of life. Current parent-report assessments for pre-school aged children are often biased, and thus increase the need for objective mental health screening tools. Leveraging digital tools to identify the behavioral signature of childhood mental disorders may enable increased intervention at the time with the highest chance of long-term impact. We present data from 84 participants (4-8 years old, 50% diagnosed with anxiety, depression, and/or ADHD) collected during a battery of mood induction tasks using the ChAMP System. Unsupervised Kohonen Self-Organizing Maps (SOM) constructed from movement and audio features indicate that age did not tend to explain clusters as consistently as gender within task-specific and cross-task SOMs. Symptom prevalence and diagnostic status also showed some evidence of clustering. Case studies suggest that high impairment (>80th percentile symptom counts) and diagnostic subtypes (ADHD-Combined) may account for most behaviorally distinct children. Based on this same dataset, we also present results from supervised modeling for the binary classification of diagnoses. Our top performing models yield moderate but promising results (ROC AUC .6-.82, TPR .36-.71, Accuracy .62-.86) on par with our previous efforts for isolated behavioral tasks. Enhancing features, tuning model parameters, and incorporating additional wearable sensor data will continue to enable the rapid progression towards the discovery of digital phenotypes of childhood mental health.Clinical Relevance- This work advances the use of wearables for detecting childhood mental health disorders.

Adult Mental Health, Substance Use Disorders, and Functional Outcomes of Children Resilient to Early Adversity.

OBJECTIVE: Some children are unaffected by mental illness despite exposure to childhood adversity. These children are typically considered resilient. The objective of this study was to follow up such resilient children in adulthood to characterize mental health status, substance use, and functional outcomes. METHODS: The analysis was based on the prospective, representative Great Smoky Mountains Study (N=1,420). Participants were assessed for psychiatric disorders and exposure to adversity with the structured Child and Adolescent Psychiatric Assessment interview up to eight times in childhood (ages 9-16; 6,674 observations). In total, 1,266 participants (86.3%) were followed up in adulthood at ages 25 and 30 to assess psychiatric disorders, substance use disorders, and functional outcomes. RESULTS: Seventy-five percent of the sample had met criteria for a psychiatric disorder or displayed subthreshold psychiatric problems by age 16. The number of adverse childhood experiences was strongly associated with childhood psychiatric status. Of children exposed to multiple adversities (N=650), 12.2% (N=63) did not display psychiatric problems. This group meets common definitions of childhood resilience. In adulthood, these individuals showing childhood resilience had greater risk of anxiety (risk ratio=2.9, 95% CI=1.0-9.1) and depressive (risk ratio=4.5, 95% CI=1.1-16.7) disorders, as well as worse physical health (means ratio=0.7, 95% CI=0.5-0.9) and financial or educational functioning (means ratio=0.6, 95% CI=0.5-0.7), compared with individuals exposed to fewer childhood adversities. These individuals showing childhood resilience did not have elevated risk for substance use disorders. CONCLUSIONS: Resilience to childhood adversity was uncommon. Individuals who appeared resilient in childhood were at risk for delayed poorer outcomes in adulthood. Public health efforts should prioritize minimizing early adversity exposure over promoting resilience.

The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health.

Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.

Parental supervision positively impacts children's economic prospects two decades later: A prospective longitudinal study.

IMPORTANCE: Upward income mobility is associated with better health outcomes and reduced stress. However, opportunities are unequally distributed, particularly so for those in rural communities and whose family have lower educational attainment. OBJECTIVE: To test the impact of parental supervision on their children's income two decades later adjusting for parental economic and educational status. DESIGN: This study is a longitudinal, representative cohort study. From 1993-2000, annual assessments of 1,420 children were completed until age 16, then followed up at age 35, 2018-2021, for further assessment. Models tested direct effects of parental supervision on child income, and indirect effects via child educational attainment. SETTING: This study is an ongoing longitudinal population-based study of families in 11 predominately rural counties of the Southeastern U.S. PARTICIPANTS: About 8% of the residents and sample are African American and fewer than 1% are Hispanic. American Indians make up 4% of the population in study but were oversampled to make up 25% of the sample. 49% of the 1,420 participants are female. MAIN OUTCOMES AND MEASURES: 1258 children and parents were assessed for sex, race/ethnicity, household income, parent educational attainment, family structure, child behavioral problems, and parental supervision. The children were followed up at age 35 to assess their household income and educational attainment. RESULTS: Parental educational attainment, income, and family structure were strongly associated with their children's household income at age 35 (e.g., r = .392, p < .05). Parental supervision of the child was associated with increased household income for the child at age 35, adjusting for SES of the family of origin. Children of parents who did not engage in adequate supervision earned approximately $14,000 less/year (i.e., ~13% of the sample's median household income) than those who did. The association of parental supervision and child income at 35 was mediated by the child's educational attainment. CONCLUSION AND RELEVANCE: This study suggests adequate parental supervision during early adolescence is associated with children's economic prospects two decades later, in part by improving their educational prospects. This is particularly important in areas such as rural Southeast U.S.

UVM KID Study: Identifying Multimodal Features and Optimizing Wearable Instrumentation to Detect Child Anxiety.

Anxiety and depression, collectively known as internalizing disorders, begin as early as the preschool years and impact nearly 1 out of every 5 children. Left undiagnosed and untreated, childhood internalizing disorders predict later health problems including substance abuse, development of comorbid psychopathology, increased risk for suicide, and substantial functional impairment. Current diagnostic procedures require access to clinical experts, take considerable time to complete, and inherently assume that child symptoms are observable by caregivers. Multi-modal wearable sensors may enable development of rapid point-of-care diagnostics that address these challenges. Building on our prior work, here we present an assessment battery for the development of a digital phenotype for internalizing disorders in young children and an early feasibility case study of multi-modal wearable sensor data from two participants, one of whom has been clinically diagnosed with an internalizing disorder. Results lend support that sacral movement responses and R-R interval during a short stress-induction task may facilitate child diagnosis. Multi-modal sensors measuring movement and surface biopotentials of the chest and trapezius are also shown to have significant redundancy, introducing the potential for sensor optimization moving forward. Future work aims to further optimize sensor placement, signals, features, and assessments to enable deployment in clinical practice. Clinical Relevance- This work considers the development and optimization of technologies for improving the identification of children with internalizing disorders.